CN109840308A - A kind of region wind power probability forecast method and system - Google Patents
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Abstract
The present invention provides a kind of region wind power probability forecast method and system, include: the forecast power for acquiring object time wind power plant, is concentrated from the analog sample obtained based on the joint probability distribution model constructed in advance and filter out the condition sample set for meeting object time wind-powered electricity generation field prediction power grade；The condition sample set is fitted to obtain conditional probability distribution function；Probability forecast section and quantile forecast ensemble are extracted based on the conditional probability distribution function.Technical solution provided by the invention extracts the condition sample set for meeting wind power probabilistic forecasting condition according to the joint probability distribution model of foundation, constructs conditional probability distribution function according to condition sample set, greatly reduces difficulty in computation, improve work efficiency.
Description
Technical Field
The invention belongs to the field of new energy power generation, and particularly relates to a regional wind power probability forecasting method and system.
Background
The wind energy resource has the characteristics of volatility and intermittence, so that the prediction precision is limited, and the uncertain distribution of wind power variables needs to be considered in the decision of power markets and power dispatching to obtain more economic and reasonable results. Therefore, a probabilistic forecasting method capable of reflecting the uncertainty of power forecasting is widely researched. When the research object is the forecast power of a plurality of regional wind power plants, a complex correlation relationship exists among random variables in a high-dimensional random vector, how to accurately fit the correlation structure relationship achieves the fitting effect of multivariate distribution of the random vector, and the accuracy of the extracted conditional probability forecast is further influenced.
A traditional correlation modeling method adopts a Gaussian Copula model for construction, but the selected fitting function is single, and the modeling precision of complex correlation is insufficient.
In the conventional probability forecasting method, when the total power probability of the region is forecasted, the number of samples meeting the target point condition is limited, and the probability forecasting effect is poor.
Disclosure of Invention
According to the method, the R-vine Copula function is selected for correlation modeling, the modeling precision is improved, the joint probability distribution model is obtained according to the correlation model and used for probability prediction of the power of the wind power plant, and the working efficiency is improved.
The invention provides a regional wind power probability forecasting method, which comprises the following steps:
acquiring forecast power of a wind power plant at a target moment;
screening out a condition sample set which accords with the forecast power level of the wind power plant at a target moment from a simulation sample set obtained based on a pre-constructed joint probability distribution model;
fitting the conditional sample set to obtain a conditional probability distribution function;
and extracting a probability forecast interval and a quantile forecast set based on the conditional probability distribution function.
The construction of the joint probability distribution model comprises the following steps:
constructing a random vector based on historical data of the wind power plant;
performing edge distribution fitting on random variables of the random vectors to obtain an edge cumulative distribution function;
obtaining a correlation vector according to the edge cumulative distribution function and the random vector;
determining an R-vine copula model according to the relevance vector;
obtaining a joint probability distribution model according to the R-vine copula model and the edge cumulative distribution function of each random variable;
the historical data includes: historical forecast power and historical forecast error.
The method comprises the following steps of constructing a random vector based on historical data of the wind power plant, wherein the random vector comprises the following steps:
and constructing a matrix comprising t time data by taking the data at the same time in the historical data as a row, and representing the matrix by using a random vector.
Determining an R-vine copula model according to the relevance vector comprises the following steps:
calculating Kendall rank correlation coefficients between every two variables in the correlation vector;
selecting a spanning tree structure which meets the Kendall rank correlation coefficient sum maximization;
a binary copula function is determined for each edge in the spanning tree and parameter estimation is performed.
The method for obtaining the simulation sample set based on the pre-constructed joint probability distribution model comprises the following steps:
generating independent random vectors meeting the uniform distribution at will;
generating a random vector of correlation according to the independent random vector and the R-vine Copula model;
and obtaining a target random vector from the random vector of the correlation according to the inverse function of the edge cumulative distribution function, and taking the target random vector as a simulation sample set.
And fitting the condition sample set and fitting the edge distribution of the random variable of the random vector by adopting a kernel density estimation method.
The invention provides a regional wind power probability forecasting system, which comprises:
the model construction module is used for constructing a joint probability distribution model in advance;
the collection module is used for collecting the forecast power of the wind power plant at a target moment;
the system comprises a conditional sample module, a power source module and a power source module, wherein the conditional sample module is used for screening out a conditional sample set which accords with the forecast power of a wind power plant at a target moment from a simulated sample set obtained based on a pre-constructed joint probability distribution model;
the fitting module is used for fitting the conditional sample set to obtain a conditional probability distribution function;
and the forecasting module is used for extracting a probability forecasting interval and a quantile forecasting set based on the conditional probability distribution function.
The model building module comprises:
the random vector unit is used for constructing a random vector based on historical data of the wind power plant;
the edge distribution fitting unit is used for performing edge distribution fitting on the random variable of the random vector to obtain an edge cumulative distribution function;
the correlation vector unit is used for obtaining a correlation vector according to the edge cumulative distribution function and the random vector;
the R-vine copula model unit is used for determining an R-vine copula model according to the relevance vector;
the joint probability distribution model unit is used for obtaining a joint probability distribution model according to the R-vine copula model and the edge cumulative distribution function of each random variable;
the historical data includes: historical forecast power and historical forecast error.
The condition sample module comprises:
a first generating unit for arbitrarily generating independent random vectors satisfying uniform distribution;
the second generation unit is used for generating a random vector of correlation according to the independent random vector and an R-vine Copula model;
the sample determining unit is used for obtaining a target random vector from the random vector of the correlation according to an inverse function of the edge cumulative distribution function, and the target random vector is taken as a simulation sample set;
and the screening unit is used for screening out a condition sample set which accords with the forecast power of the wind power plant at the target moment from the simulation sample set.
The R-vine copula model unit comprises:
the coefficient calculating subunit is used for calculating Kendall rank correlation coefficients between every two variables in the correlation vector;
the spanning tree subunit is used for selecting a spanning tree structure which meets the Kendall rank correlation coefficient sum maximization;
and the model determining subunit is used for determining a binary copula function for each edge in the spanning tree and performing parameter estimation.
Compared with the closest prior art, the technical scheme provided by the invention has the following beneficial effects:
according to the technical scheme provided by the invention, the condition sample set meeting the wind power probability prediction condition is extracted according to the establishment of the joint probability distribution model, and the condition probability distribution function is constructed according to the condition sample set, so that the calculation difficulty is greatly reduced, and the working efficiency is improved;
according to the technical scheme provided by the invention, the splitting of a high-dimensional related structure and the selection and fitting of various binary Copula functions can be realized by adopting the R-vine Copula function, so that the flexibility and the accuracy of the correlation modeling are improved.
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FIG. 1 is a flow chart of a regional wind power probability forecasting method of the present invention;
fig. 2 is an overall flowchart of a regional wind power probability forecasting method according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
the first embodiment is as follows:
fig. 1 is a flowchart of a regional wind power probability forecasting method according to the present invention, and as shown in fig. 1, the regional wind power probability forecasting method according to the present invention may include:
acquiring forecast power of a wind power plant at a target moment;
screening out a condition sample set which accords with the forecast power level of the wind power plant at a target moment from a simulation sample set obtained based on a pre-constructed joint probability distribution model;
fitting the conditional sample set to obtain a conditional probability distribution function;
and extracting a probability forecast interval and a quantile forecast set based on the conditional probability distribution function.
The construction of the joint probability distribution model comprises the following steps:
constructing a random vector based on historical data of the wind power plant;
performing edge distribution fitting on random variables of the random vectors to obtain an edge cumulative distribution function;
obtaining a correlation vector according to the edge cumulative distribution function and the random vector;
determining an R-vine copula model according to the relevance vector;
obtaining a joint probability distribution model according to the R-vine copula model and the edge cumulative distribution function of each random variable;
the historical data includes: historical forecast power and historical forecast error.
The method comprises the following steps of constructing a random vector based on historical data of the wind power plant, wherein the random vector comprises the following steps:
and constructing a matrix comprising t time data by taking the data at the same time in the historical data as a row, and representing the matrix by using a random vector.
Determining an R-vine copula model according to the relevance vector comprises the following steps:
calculating Kendall rank correlation coefficients between every two variables in the correlation vector;
selecting a spanning tree structure which meets the Kendall rank correlation coefficient sum maximization;
a binary copula function is determined for each edge in the spanning tree and parameter estimation is performed.
The method for obtaining the simulation sample set based on the pre-constructed joint probability distribution model comprises the following steps:
generating an independent random vector meeting the uniform distribution at will;
generating a random vector of correlation according to the independent random vector and the R-vine Copula model;
and obtaining a target random vector from the random vector of the correlation according to the inverse function of the edge cumulative distribution function, and taking the target random vector as a simulation sample set.
And fitting the condition sample set and fitting the edge distribution of the random variable of the random vector by adopting a kernel density estimation method.
Example two:
based on the same inventive concept, the regional wind power probability forecasting system provided by the invention can comprise:
the model construction module is used for constructing a joint probability distribution model in advance;
the collection module is used for collecting the forecast power of the wind power plant at a target moment;
the system comprises a conditional sample module, a power source module and a power source module, wherein the conditional sample module is used for screening out a conditional sample set which accords with the forecast power of a wind power plant at a target moment from a simulated sample set obtained based on a pre-constructed joint probability distribution model;
the fitting module is used for fitting the conditional sample set to obtain a conditional probability distribution function;
and the forecasting module is used for extracting a probability forecasting interval and a quantile forecasting set based on the conditional probability distribution function.
The model building module comprises:
the random vector unit is used for constructing a random vector based on historical data of the wind power plant;
the edge distribution fitting unit is used for performing edge distribution fitting on the random variable of the random vector to obtain an edge cumulative distribution function;
the correlation vector unit is used for obtaining a correlation vector according to the edge cumulative distribution function and the random vector;
the R-vine copula model unit is used for determining an R-vine copula model according to the relevance vector;
the joint probability distribution model unit is used for obtaining a joint probability distribution model according to the R-vine copula model and the edge cumulative distribution function of each random variable;
the historical data includes: historical forecast power and historical forecast error.
The condition sample module comprises:
a first generating unit for arbitrarily generating independent random vectors satisfying uniform distribution;
the second generation unit is used for generating a random vector of correlation according to the independent random vector and an R-vine Copula model;
the sample determining unit is used for obtaining a target random vector from the random vector of the correlation according to an inverse function of the edge cumulative distribution function, and the target random vector is taken as a simulation sample set;
and the screening unit is used for screening out a condition sample set which accords with the forecast power of the wind power plant at the target moment from the simulation sample set.
The R-vine copula model unit comprises:
the coefficient calculating subunit is used for calculating Kendall rank correlation coefficients between every two variables in the correlation vector;
the spanning tree subunit is used for selecting a spanning tree structure which meets the Kendall rank correlation coefficient sum maximization;
and the model determining subunit is used for determining a binary copula function for each edge in the spanning tree and performing parameter estimation.
The random vector unit includes:
and constructing a matrix comprising t time data by taking the data at the same time as a row, and representing the matrix by using a random vector.
Example three:
a regional wind power probability forecasting method can comprise the following steps:
constructing an R-vine copula model and carrying out wind power probability forecasting based on the R-vine copula model;
fig. 2 is an overall flowchart of a regional wind power probability forecasting method, and as shown in fig. 2, the constructing of the R-vinecopula model may include:
step 1-1: inputting d-dimensional sample [ X ]1,...,Xd]＝[P1,...,Pn,E1,...,En]；
Step 1-2: performing edge fitting on elements in the sample to obtain a fitted edge cumulative distribution function;
step 1-3: removal of [ X ]1,...,Xd]The influence of the middle edge distribution obtains d-dimensional sample data [ U ]1,…,Ud]；
Step 1-4: based ond dimension sample data U1,…,Ud]Screening out a maximum spanning tree according to a correlation coefficient maximization principle, and obtaining a correlation variable pair in the tree; and (3) checking whether the relevant variable pairs are independent, if so, judging whether all the trees are generated, otherwise, screening out a binary Copula function required by the trees, estimating parameters, and then judging whether all the trees are generated, if all the trees are generated, finishing the construction of the R-vine Copula model, and if not, repeating the step until all the trees are generated.
After all the trees are generated, performing a wind power probability forecasting step based on an R-vine copula model, where the performing the wind power probability forecasting based on the R-vine copula model may include:
step 2-1: based on an R-vine copula model, obtaining a simulation sample set S by a random sampling method;
step 2-2: screening samples which accord with a forecast target point in the S according to the probability forecast condition;
step 2-3: and performing conditional probability density distribution fitting on the screened samples, and outputting a conditional probability density function.
Specifically, the method comprises the following steps:
step 1-1: inputting d-dimensional sample [ X ]1,...,Xd]＝[P1,...,Pn,E1,...,En]The generating of the d-dimensional samples in (1) may include:
the objects to be modeled are the forecast power P and forecast error E of each wind farm in the region, (5-1) gives an example of n wind farms, wherein each row in the matrix corresponds to data at the same time, the data at t moments are total according to the size of the sample set, the random variable corresponding to each column is represented by upper-case P and E, and the random vector X which is uniformly represented as d-dimension for convenience of expression is represented as (X is the random vector X ═ which is uniformly represented as d-dimension1,…,Xd)。
Step 1-2: performing edge fitting on elements in the sample to obtain a fitted edge distribution model, which may include:
considering that each edge variable is difficult to determine a uniform parameter distribution and the empirical distribution function is discontinuous, the method adopts non-parameter distribution of kernel density estimation to carry out edge distribution fitting, and the probability density function of fitting is shown as a formula (5-2).
Wherein h is the bandwidth, K represents the kernel function, the invention adopts the Gaussian kernel, the expression is shown as (5-3), n represents the sample size, and X represents the sample data.
Step 1-3: removal of [ X ]1,...,Xd]The influence of the middle edge distribution obtains d-dimensional sample data [ U ]1,…,Ud]The method comprises the following steps:
corresponding edge cumulative distribution function according to estimationThe sum X can be obtained as a correlation vector U ═ where the influence of edge distribution is removed (U ═ X1,...,Ud) The corresponding edge distribution satisfies the uniform distribution.
Converting X into U through an edge cumulative distribution function, stripping the influence of edge distribution, and only considering the related structure as the formula
Wherein,is the inverse of the edge cumulative distribution function CDF, U ═ U1,...,Ud)∈[0,1]d。
Step 1-4: based on d dimension sample data [ U ]1,…,Ud]Screening a maximum spanning tree according to a correlation coefficient maximization principle, obtaining a correlation variable pair in the tree, checking whether the correlation variable pair is independent, if so, judging whether all the trees are generated, otherwise, screening a binary Copula function required by the tree, performing parameter estimation, judging whether all the trees are generated, if all the trees are generated, completing the construction of an R-vine Copula model, and if not, repeating the steps until all the trees are generated, wherein the steps can comprise:
determining that the corresponding R-vine copula needs to complete the following three tasks according to the U obtained in the last step:
1. the R-vine structure is selected, i.e. the constraint condition set { j (e), k (e) D (e) } of each tree is given.
2. And selecting a proper binary copula type for the binary random variable corresponding to each edge e in the R-vine.
3. The parameters of each binary copula function are estimated.
The three items are often combined together in practical application, and the construction of the R-vine copula is realized one by one according to a successive method. The algorithm flow is as follows:
based on the consideration of model calculation cost, before binary Copula fitting and parameter estimation, independence test is introduced, an independent Copula function is directly adopted for a random variable pair close to independence, and the calculation complexity and the accuracy of a corresponding model are controlled according to the magnitude of the significance degree.
Specifically, the wind power probability forecasting based on the R-vine copula model may include:
the joint probability distribution function is obtained according to the R-vine copula model, although the joint probability distribution function is a continuously analyzed mathematical expression, when the regional total power probability is predicted, a calculated object needs to be obtained through multiple integral calculation, and the integral calculation is difficult under the condition that the integral function is complex and has no engineering practical significance, so that the condition probability density result meeting the condition is fit by generating enough data samples according to the obtained joint probability distribution function.
First, a computer arbitrarily generates a signal satisfying a uniform distribution U (0,1)dD-dimensional independent random vector W ═ W1,...,Wd)。
Then generating random vector of correlation by (5-4)
Finally, the inverse of the distribution function is accumulated according to the fitted edges, i.e.FromSolving a target random vectorAfter obtaining a sufficient number of modesSimulation sample(simulation sample set S), it is considered thatThe method comprises the steps of screening sample points of total power of regions meeting conditions according to the conditions of the forecast power grades of all wind power plants in the regions to form a set C, then constructing a continuous probability distribution function for the samples in the C by adopting a kernel density estimation method (the same as formula 5-1), and extracting forecast interval results with different confidence degrees according to actual requirements.
Obtaining a joint probability distribution function according to the R-vine copula model, which may include:
regular vine (R-vine) V is a d-element Regular vine, the collection of its edges being denoted as E (V) ═ E1∪…∪Ei∪…∪Ed-1In which EiI-1, …, d-1 represents the ith tree TiThe set of edges of (a). Regular vines need to satisfy the following three conditions:
4)V＝{T1，…,Td-1i.e. a set of d-1 trees.
5)T1Is N11, …, d, and the set of edges is E1(ii) a And for i 2, …, d-1, TiIs NiThe set of edges is EiThe condition N needs to be satisfiedi＝Ei-1。
6) (proximity principle) for i 2, …, d-1, { a, b }. epsilon.EiAnd # (a △ b) ═ 2, where △ denotes the peer-to-peer difference for the compute set and # denotes the potential for the compute set.
7) Regular vine V with dimension d is composed of d-1 trees { T }1，…,Td-1Is composed of a set of nodes { N }1，…,Nd-1In which N is collected11, …, d, corresponding to the initial d random variable numbers in the correlation modeling.The set of edges for V is denoted as { E1，…,Ed-1treeTiSet of edges E ofiOne edge e in (a) may be represented in the form of e ═ j (e), k (e) | d (e), where { j (e), k (e), j (e) ≠ k (e) } is referred to as the conditioning set, and d (e) is referred to as the conditioning set, the elements in both sets being composed of {1, …, d }. According to the proximity principle, E is defined by Ei-1The two corresponding sides a ═ j (a), k (a) | D (a), b ═ j (b), k (b) | D (b) are determined, a and b are determined at Ti-1There is a common node in the three-edge relationship, the three-edge relationship is expressed as the following two relationships
D(e):＝U(a)∩U(b) (3-4)
{j(e),k(e)}:＝U(a)∪U(b)\D(e)， (3-5)
Where u (e) { j (e), k (e), d (e) } denotes the complete set of elements contained in e, encompassing all the elements in the conditioning set and the conditioned set. In addition, for E1The edge in (e) is in the form of e ═ j (e), k (e), because the conditioning set d (e) is an empty set at this time.
When the marking rule of the edge is clear, the binary copula density corresponding to e can be expressed as cj(e),k(e)|D(e)。
In combination with the above, a formula of the multi-dimensional joint density distribution described by the regular rattan structure is given:
which corresponds to a d-dimensional random variable X: (X)1,...,Xd) The cumulative distribution function of the edge is fk,k＝1,...,d，XD(e)Denotes the subset of X specified by D (e). Wherein, the conditional distribution function F (x) is a variable in one binary copula in the ith tree in the formulaj(e)|xD(e)) And F (x)k(e)|xD(e)) Can be calculated by copula function C and corresponding conditional distribution function F of the estimated parameters in the i-1 th tree.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: numerous variations, modifications, and equivalents will occur to those skilled in the art upon reading the present application and are within the scope of the claims appended hereto.
Claims (10)
1. A regional wind power probability forecasting method is characterized by comprising the following steps:
acquiring forecast power of a wind power plant at a target moment;
screening out a condition sample set which accords with the forecast power level of the wind power plant at a target moment from a simulation sample set obtained based on a pre-constructed joint probability distribution model;
fitting the conditional sample set to obtain a conditional probability distribution function;
and extracting a probability forecast interval and a quantile forecast set based on the conditional probability distribution function.
2. The regional wind power probability forecasting method of claim 1, wherein the building of the joint probability distribution model comprises:
constructing a random vector based on historical data of the wind power plant;
performing edge distribution fitting on random variables of the random vectors to obtain an edge cumulative distribution function;
obtaining a correlation vector according to the edge cumulative distribution function and the random vector;
determining an R-vine copula model according to the relevance vector;
obtaining a joint probability distribution model according to the R-vine copula model and the edge cumulative distribution function of each random variable;
the historical data includes: historical forecast power and historical forecast error.
3. The regional wind power probability forecasting method of claim 2, characterized in that the construction of the random vector based on the historical data of the wind farm comprises:
and constructing a matrix comprising t time data by taking the data at the same time in the historical data as a row, and representing the matrix by using a random vector.
4. The regional wind power probability forecasting method of claim 2, wherein the determining an R-vine copula model according to the relevance vector comprises:
calculating Kendall rank correlation coefficients between every two variables in the correlation vector;
selecting a spanning tree structure which meets the Kendall rank correlation coefficient sum maximization;
a binary copula function is determined for each edge in the spanning tree and parameter estimation is performed.
5. The regional wind power probability forecasting method of claim 2, wherein the obtaining of the simulation sample set based on the pre-constructed joint probability distribution model comprises:
generating independent random vectors meeting the uniform distribution at will;
generating a random vector of correlation according to the independent random vector and the R-vine Copula model;
and obtaining a target random vector from the random vector of the correlation according to the inverse function of the edge cumulative distribution function, and taking the target random vector as a simulation sample set.
6. The regional wind power probability forecasting method of claim 2, characterized in that fitting the condition sample set and fitting the edge distribution of the random variables of the random vector both adopt a kernel density estimation method.
7. A regional wind power probability forecasting system is characterized by comprising:
the model construction module is used for constructing a joint probability distribution model in advance;
the collection module is used for collecting the forecast power of the wind power plant at a target moment;
the system comprises a conditional sample module, a power source module and a power source module, wherein the conditional sample module is used for screening out a conditional sample set which accords with the forecast power of a wind power plant at a target moment from a simulated sample set obtained based on a pre-constructed joint probability distribution model;
the fitting module is used for fitting the conditional sample set to obtain a conditional probability distribution function;
and the forecasting module is used for extracting a probability forecasting interval and a quantile forecasting set based on the conditional probability distribution function.
8. The regional wind power probability forecasting system of claim 7, wherein the model building module comprises:
the random vector unit is used for constructing a random vector based on historical data of the wind power plant;
the edge distribution fitting unit is used for performing edge distribution fitting on the random variable of the random vector to obtain an edge cumulative distribution function;
the correlation vector unit is used for obtaining a correlation vector according to the edge cumulative distribution function and the random vector;
the R-vine copula model unit is used for determining an R-vine copula model according to the relevance vector;
the joint probability distribution model unit is used for obtaining a joint probability distribution model according to the R-vine copula model and the edge cumulative distribution function of each random variable;
the historical data includes: historical forecast power and historical forecast error.
9. The regional wind power probability forecasting system of claim 8, wherein the condition sample module comprises:
a first generating unit for arbitrarily generating independent random vectors satisfying uniform distribution;
the second generation unit is used for generating a random vector of correlation according to the independent random vector and an R-vine Copula model;
the sample determining unit is used for obtaining a target random vector from the random vector of the correlation according to an inverse function of the edge cumulative distribution function, and the target random vector is taken as a simulation sample set;
and the screening unit is used for screening out a condition sample set which accords with the forecast power of the wind power plant at the target moment from the simulation sample set.
10. The regional wind power probability forecasting system of claim 8, wherein the R-vine copula model unit comprises:
the coefficient calculating subunit is used for calculating Kendall rank correlation coefficients between every two variables in the correlation vector;
the spanning tree subunit is used for selecting a spanning tree structure which meets the Kendall rank correlation coefficient sum maximization;
and the model determining subunit is used for determining a binary copula function for each edge in the spanning tree and performing parameter estimation.
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